scholarly journals ARTIFICIAL NEURAL NETWORK IN THE MODELLING OF THE EFFECT OF CHROMIUM DOPANTS ON THE MECHANICAL PROPERTIES OF AL-4%CU ALLOY

2019 ◽  
Vol 25 (1) ◽  
pp. 16-24
Author(s):  
EYERE EMAGBETERE ◽  
OGHENEKOWHO PETER ARUOTURE ◽  
FESTUS IFEANYI ASHIEDU
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Percentage Elongation ◽  
Cu Alloy ◽  
Simulation Results ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Strong Agreement

Artificial Neural Network (ANN) was used to model the effect of Chromium dopants on the mechanical properties duralumin (Al-4 %Cu). The results showed that the hardness, yield strength, and ultimate tensile strength increased, while the energy absorbed and percentage elongation decreased, with increasing %wt of Chromium dopants. Simulation results of ANN show strong agreement with experimental values, having satisfactory R-values of Mean Square Error. ANN can suitably be used to predict the mechanical properties of Al-4%Cu doped with Chromium.

scholarly journals ARTIFICIAL NEURAL NETWORK IN THE MODELLING OF THE EFFECT OF CHROMIUM DOPANTS ON THE MECHANICAL PROPERTIES OF AL-4%CU ALLOY

2019 ◽  
Vol 25 (1) ◽  
Author(s):  
EMAGBETERE EYERE ◽  
PETER ARUOTURE OGHENEKOWHO ◽  
IFEANYI ASHIEDU FESTUS
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Percentage Elongation ◽  
Cu Alloy ◽  
Simulation Results ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Strong Agreement

Artificial Neural Network (ANN) was used to model the effect of Chromium dopants on the mechanical properties duralumin (Al-4 %Cu). The results showed that the hardness, yield strength, and ultimate tensile strength increased, while the energy absorbed and percentage elongation decreased, with increasing %wt of Chromium dopants. Simulation results of ANN show strong agreement with experimental values, having satisfactory R-values of Mean Square Error. ANN can suitably be used to predict the mechanical properties of Al-4%Cu doped with Chromium.

Artificial Neural Network Model: Prediction of Mechanical Properties in Beta-Titanium Biomaterial

2013 ◽  
Vol 367 ◽  
pp. 40-44 ◽  
Author(s):  
K.V. Sudhakar ◽  
M.E. Haque
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Thermal Processing ◽  
Beta Titanium Alloy ◽  
Beta Titanium ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann ◽  
Experimental Values

Theoretical prediction of mechanical properties using Artificial Neural Network (ANN) of a new beta-titanium alloy was investigated and compared with the experimental results obtained as a function of heat treatment. The cold worked biomaterial was subjected to different thermal processing cycles. The experimental values of mechanical properties were determined using MTS Landmark-servo hydraulic UTS machine. The new beta-titanium alloy demonstrated an excellent combination of strength and ductility for β-annealing and solution treatment plus aging thermal processing treatments. This data was used to train an artificial neural network (ANN) model to predict hardness. The predicted hardness values were found to demonstrate very good agreement with the experimental values.

Bridging Precipitate Microstructure and Mechanical Hardness of Nickel-Based Superalloy Through Artificial Neural Network Model

2020 ◽  
Author(s):  
Yangping Li ◽  
Yangyi Liu ◽  
Sihua Luo ◽  
Zi Wang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Cooling Rate ◽  
Volume Fraction ◽  
Sem Images ◽  
Mechanical Hardness ◽  
Nickel Based Superalloy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract The attractive mechanical properties of nickel-based superalloys primarily arise from an assembly of γ′ precipitates with desirable size, volume fraction, morphology and spatial distribution. In addition, the solutioning cooling rate after super solvus heat treatment is critical for controlling the features of γ′ precipitates. However, the correlation between these multidimensional parameters and mechanical hardness has not been well established to date. Scanning electron microscope (SEM) images with different γ′ precipitates were investigated in this study, and artificial neural network (ANN) method was used to build a microstructure-mechanical property model. The critical step in this work is to extract different microstructural features from hundreds of SEM images. In order to improve the accuracy of prediction, the cooling rate was also considered as the input. In this work, the methodology was proved to be capable of bridging microstructural features and mechanical properties under the inspiration of material genome spirit.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

Prediction of Wear Behavior in Porous Sintered Steels: Artificial Neural Network Approach

2018 ◽  
Vol 18 (2) ◽  
pp. 111-115
Author(s):  
Hassan Abdoos ◽  
Ahmad Tayebi ◽  
Meysam Bayat
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Wear Resistance ◽  
Wear Behavior ◽  
Affecting Factors ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Comparison Of The Results

Abstract Due to the increasing usage of powder metallurgy (PM), there is a demand to evaluate and improve the mechanical properties of PM parts. One of the most important mechanical properties is wear behavior, especially in parts that are in contact with each other. Therefore, the choice of materials and select manufacturing parameters are very important to achieve proper wear behavior. So, prediction of wear resistance is important in PM parts. In this paper, we try to investigate and predict the wear resistance (volume loss) of PM porous steels according to the affecting factors such as: density, force and sliding distance by artificial neural network (ANN). ANN training was done by a multilayer perceptron procedure. The comparison of the results estimated by the ANN with the experimental data shows their proper matching. This issue confirms the efficiency of using method for prediction of wear resistance in PM steel parts.

Improving the Mechanical Properties of Wire-Rope Silk Scaffold by Artificial Neural Network in Tendon and Ligament Tissue Engineering

2015 ◽  
Vol 10 (3) ◽  
pp. 155892501501000 ◽  
Author(s):  
Elham Naghashzargar ◽  
Dariush Semnani ◽  
Saeed Karbasi
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Wire Rope ◽  
Elongation At Break ◽  
Input Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Silk Yarn

Finding an appropriate model to assess and evaluate mechanical properties in tissue engineered scaffolds is a challenging issue. In this research, a structurally based model was applied to analyze the mechanics of engineered tendon and ligament. Major attempts were made to find the optimum mechanical properties of silk wire-rope scaffold by using the back propagation artificial neural network (ANN) method. Different samples of wire-rope scaffolds were fabricated according to Taguchi experimental design. The number of filaments and twist in each layer of the four layered wire-rope silk yarn were considered as the input parameters in the model. The output parameters included the mechanical properties which consisted of UTS, elongation at break, and stiffness. Finally, sensitivity analysis on input data showed that the number of filaments and the number of twists in the fourth layer are less important than other input parameters.

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

2021 ◽  
pp. 095440892110641
Author(s):  
Gaurav Kumar ◽  
Shyama Prasad Saha ◽  
Shilpi Ghosh ◽  
Pranab Kumar Mondal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Enzyme Production ◽  
Xylanase Activity ◽  
Ann Model ◽  
Process Variables ◽  
Xylanase Production ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values

The industrial production of enzymes is generally optimized by one-factor-at-a-time (OFAT) approach. However, enzyme production by the method involves submerged or solid-state fermentation, which is laborious and time-consuming and it does not consider interactions among process variables. Artificial neural network (ANN) offers enormous potential for modelling biochemical processes and it allows rational prediction of process variables of enzyme production. In the present work, ANN has been used to predict the experimental values of xylanase production optimized by OFAT. This makes the reported ANN model to predict further optimal values for different input conditions. Both single hidden layered (6-3-1) and double hidden layered (6-12-12-1) were able to closely predict the actual values with MSE equals to 0.004566 and 0.002156, respectively. The study also uses multiple linear regression (MLR) analysis to calculate and compare the outcome with ANN predicted xylanase activity, and to establish a parametric sensitivity.

scholarly journals Creep analysis of bimaterial microcantilever beam for sensing device using artificial neural network (ANN)

2017 ◽  
Vol 23 (1&2) ◽  
pp. 89 ◽  
Author(s):  
WaiChi Wong ◽  
HingWah Lee ◽  
Ishak A. Azid ◽  
K.N. Seetharamu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Feed Forward Back Propagation ◽  
The Neural Network ◽  
Creep Analysis ◽  
Simulation Results ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Microcantilever Beam

In this study, a feed-forward back-propagation Artificial Neural Network (ANN) is used to predict the stress relaxation and behavior of creep for bimaterial microcantilever beam for sensing device. Results obtained from ANSYS® 8.1 finite element (FE) simulations, which show good agreement with experimental work [1], is used to train the neural network. Parametric studies are carried out to analyze the effects of creep on the microcantilever beam in term of curvature and stress deve loped with time. It is shown that ANN accurately predicts the stress level for the microcantilever beam using the trained ANSYS® simulation results due to the fact that there is no scattered data in the FE simulation results. ANN takes a small fraction of time and effort compar ed to FE prediction.

scholarly journals PREDICTION OF THE SIZE OF NANOPARTICLES AND MICROSPORE SURFACE AREA USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 1 (1) ◽  
pp. 65
Author(s):  
Dženana Sarajlić ◽  
Layla Abdel-Ilah ◽  
Adnan Fojnica ◽  
Ahmed Osmanović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Surface Area ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Experimental Values

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

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